STOPP (Screening Tool of Older Persons Prescriptions) and START (Screening Tool to Alert doctors to Right Treatment). Consensus validation
Bibliographic record
Abstract
OBJECTIVE: Older people experience more concurrent illnesses, are prescribed more medications and suffer more adverse drug events than younger people. Many drugs predispose older people to adverse events such as falls and cognitive impairment, thus increasing morbidity and health resource utilization. At the same time, older people are often denied potentially beneficial, clinically indicated medications without a valid reason. We aimed to validate a new screening tool of older persons' prescriptions incorporating criteria for potentially inappropriate drugs called STOPP (Screening Tool of Older Persons' Prescriptions) and criteria for potentially appropriate, indicated drugs called START (Screening Tool to Alert doctors to Right, i.e. appropriate, indicated Treatment). METHODS: A Delphi consensus technique was used to establish the content validity of STOPP/START. An 18-member expert panel from academic centers in Ireland and the United Kingdom completed two rounds of the Delphi process by mail survey. Inter-rater reliability was assessed by determining the kappa-statistic for measure of agreement on 100 data-sets. RESULTS: STOPP is comprised of 65 clinically significant criteria for potentially inappropriate prescribing in older people. Each criterion is accompanied by a concise explanation as to why the prescribing practice is potentially inappropriate. START consists of 22 evidence-based prescribing indicators for commonly encountered diseases in older people. Inter-rater reliability is favorable with a kappa-coefficient of 0.75 for STOPP and 0.68 for START. CONCLUSION: STOPP/START is a valid, reliable and comprehensive screening tool that enables the prescribing physician to appraise an older patient's prescription drugs in the context of his/her concurrent diagnoses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".